http://iet.metastore.ingenta.com
1887

Performance metrics for traditional and context-aware big data recommender systems

Performance metrics for traditional and context-aware big data recommender systems

For access to this article, please select a purchase option:

Buy chapter PDF
$16.00
(plus tax if applicable)
Buy Knowledge Pack
10 chapters for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Big Data Recommender Systems - Volume 2: Application Paradigms — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Recommender System (RS) concept was coined in the mid-1990s, when researchers took interest in recommendation problems that primarily used the concept of ratings to obtain the user preferences for different items. A lot of work has been exercised and investigated in this area for recommending the most relevant information and contents to users without taking the contextual information, such as date, time, location and event. In the last few years, context-aware recommender systems (CARS) have made tremendous contributions in all domains of life and improved the recommendation process based on the contextual information along with the traditional approaches. The effectiveness of an algorithm can be measured in the sense that how efficiently it returns the recommendation to users/customers with respect to context or occasion. To assess the effectiveness and performance of any recommender algorithms completely, some common metrics are defined to assess the performance of the recommender algorithm beforehand.

Chapter Contents:

  • 4.1 Introduction
  • 4.2 CARS—a brief overview
  • 4.3 Evaluation of RSs
  • 4.3.1 Evaluation metrics
  • 4.3.1.1 Prediction accuracy metrics
  • 4.3.1.2 Usage prediction measurement/classifying accuracy metrics
  • 4.3.1.3 Rank accuracy metrics
  • 4.4 Diversity and accuracy metrics used in CARS
  • 4.4.1 How recommendation accuracy is measured in CARS?
  • 4.4.2 Diversity measurement in CARS
  • 4.5 How to choose an appropriate evaluation metrics?
  • 4.6 Conclusion
  • Acknowledgments
  • References

Inspec keywords: Big Data; ubiquitous computing; recommender systems

Other keywords: CARS; contextual information; recommender systems; performance metrics; context-aware Big Data; RS

Subjects: Information networks; Data handling techniques; Search engines

Preview this chapter:
Zoom in
Zoomout

Performance metrics for traditional and context-aware big data recommender systems, Page 1 of 2

| /docserver/preview/fulltext/books/pc/pbpc035g/PBPC035G_ch4-1.gif /docserver/preview/fulltext/books/pc/pbpc035g/PBPC035G_ch4-2.gif

Related content

content/books/10.1049/pbpc035g_ch4
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address